Asia-Pacific Journal of Regional Science最新文献

Madhya Pradesh, the central state of India, has a larger share of the population depending on the agricultural sector, mainly belonging to tribal groups and are largely marginal and small landholders. Though the sector performed a double-digit growth rate in recent years, this is skewed towards very few districts, whereas others remain neglected. These increasing disparities may lead to increased vulnerability to climate change in the backward districts of this sector. This study assessed the spatial and temporal vulnerability of the sector to climate change with the agricultural vulnerability index prepared using the IPCC approach at the district level for 5 decades (1970s–2010s). Hotspots of agricultural vulnerability where targeted policy interventions are required were located. Through spatial autocorrelation techniques, we identified whether the clustering of agricultural vulnerability exists and detected changes over the decades. This study has wide applications for the agricultural sector in Madhya Pradesh and other Indian states, which possess similar agricultural characteristics.

Rural electrification, serving as a proxy for energy access, is pivotal for economic growth in Bangladesh. This paper investigates the long-run and short-run effects of rural electrification (RELEC) on economic growth, while also considering the influence of the informal economy, and income inequality. Using an autoregressive distributed lag (ARDL) approach and analyzing data of Bangladesh economy over the period 1976–2020, the study finds that RELEC has a significant positive impact on economic growth in the long run. However, in the short run, RELEC exhibits negative effects on economic growth. FMOLS method is utilized to check the sensitivity that confirms the long run favorable impact of rural electrification on economic growth. The Informal Economy negatively affects growth, while the Gini coefficient has a positive impact in both short and long terms. Vector error correction methodology (VECM) shows bidirectional causality between growth and electrification. This unique study considers diverse determinants amid Bangladesh’s evolving economic landscape. Policymakers are urged to diversify the energy mix to meet rural electrification demand, involving private investment, boosting capacity, and fostering competition. Moreover, there is a necessity to promote various channels such as sustainable agriculture, rural industrialization, poverty reduction through which electricity access may enhance growth. The error correction term (ECT) coefficients show a rather quick adjustment process, demonstrating that the model’s adjustment mechanism is agile.

Air quality is one of the largest environmental risks for people in urban areas. Therefore, research on the economic value of air quality is necessary to reduce adverse impacts from air pollution on public health and economy for achieving sustainable cities. One of the main objectives of this research was to analyze spatial variability of air quality and spatial distribution of apartment transactions in Seoul, South Korea between January 2018 and June 2020. A second aim was to conduct spatial econometrics to determine the impacts of air quality on housing prices by proposing a spatial lag model to estimate exogeneous spatial autocorrelation of air quality in adjacent areas. Transaction data from 17,000 apartments between January 2018 and June 2020 provided strong evidence for the existence of significant effects of air quality on housing prices. As expected, all three air quality measurements, levels of PM₁₀, PM_2.5 and Comprehensive Air-quality Index (CAI), showed a negative correlation with housing transaction prices, suggesting worsening air quality leads apartments in such areas to be transacted at a discount. Moreover, the spatial model showed a strong spatial dependence between air quality in a given region and neighboring regions, and such effects led to a decrease in the price effect of air quality. Under conditions of poor air quality and its impacts on human health, demands for clean air in dense urban areas when purchasing an apartment unit are increasing especially in the post-COVID era. The results of this study can help urban planners and developers determine guidelines and spatial strategies for sustainable cities.

Balancing economic growth and carbon emissions reduction is crucial for achieving integrated development in the Yangtze River Delta (YRD) region and meeting green initiatives. This study utilized the allometric growth model to analyze the decoupling relationship between economic growth (EG) and carbon emissions (CE) in the YRD city cluster from 2000 to 2017. In addition, the geographically weighted quantile regression model (GWQR) was used to identify factors influencing this relationship. The main findings are as follows: (1) from 2000 to 2017, a V-shaped positive correlation trend was observed between EG and CE. Meanwhile, the spatial correlation level declined, with strong incidence values concentrated in the central and northern parts of the delta region. Conversely, areas with low incidence intensity were scattered across certain counties in the Anhui Province and the northwest region of Zhejiang Province. (2) From 2000 to 2017, the region witnessed a dominant I-type negative allometric growth pattern with weak economic expansion between EG and CE. In addition, most counties underwent a shift from positive allometry to negative allometry, particularly types I and II. (3) The influence of various factors on allometric growth pattern varied across counties and quantiles. Population density (POP) consistently had negative impacts at the 0.1 and 0.9 quantiles for all counties, while showing both positive and negative effects at the 0.3, 0.5, and 0.7 quantiles. Urbanization rate (URB) generally had a negative impact, except at the 0.7 quantile. The ratio of the tertiary industries to GDP (TER) had a negative effect only at the 0.1 quantile but had mixed positive and negative effects at other quantiles. Carbon sequestration of terrestrial vegetation (CSE) exhibited both positive and negative impacts at higher quantiles but consistently had a positive impact at the 0.1, 0.3, and 0.5 quantiles. These findings provide valuable insights into the complex relationship between these factors and allometric growth in different regions and quantiles, informing policy-making and sustainable development strategies.

In this article, we use a three-step procedure that combines the log t convergence test, Explanatory Spatial Data Analysis, and ordered logit regression to determine the spatio-temporal dynamics and determinants of provincial income clustering in Vietnam during the 2010–2020 period. Our findings are three-fold. First, provincial income clustering in Vietnam follows patterns of club convergence towards multiple equilibria. Seven convergence clubs encompassing 61 provinces are identified. Second, spatial autocorrelation encourages neighboring provinces to converge toward shared income equilibria. High-income clusters are observed in the Northern and Southern Key Economic Regions, while low-income clusters are concentrated in the mountainous areas of Northern Vietnam. Finally, both internal and external factors significantly affect the formation of convergence clubs. Vital internal factors include localities’ initial conditions of physical capital and structural change. Meanwhile, external factors refer to spatial externalities among neighboring provinces. We highlight spatial complementarity in physical capital accumulation and spatial competition in industrial intensification among neighboring provinces.

Job–education matching drives inclusive growth through effective human capital investment. Examination of factors that promote the smooth flow of job-education is crucial in the matching process. We examined how agglomeration affects job-education mismatches among 101,748 employed graduates of vocational secondary schools in Sekolah Menengah Kejuruan (SMK). SMK graduates are the leading cause of unemployment in Indonesia. Data were obtained from the National Labor Force Survey (Sakernas) conducted between 2017 and 2019. This study revealed three different types of job-education mismatches: (1) overeducated workers (level of education exceeds the requirements of their job); (2) horizontally mismatched workers (skills do not align with the job requirements); and (3) workers who are both overeducated and horizontally mismatched, which defines a real mismatch. Employing the job-analysis approach, a 13.58 percent incidence of overeducation and a 61.58 percent incidence of horizontal mismatch among SMK graduates was determined. More than half of these graduates work in jobs where they lack the necessary skills. By assessing the two types of job-education mismatches, we determined that 10.13 percent were real mismatched workers. These workers endured major challenges as they simultaneously suffered horizontal mismatch and overeducation. Dealing with endogeneity and sample selection biases, we showed that agglomeration actively promotes the matching process between occupation and education. Adding 100 workers per square kilometer reduced the probability of overeducation by 0.15 percent, horizontal mismatch by 0.19 percent, and real mismatch by 0.1 percent. Indonesian agglomeration areas outside Java (Mebidangro and Sarbagita) are more effective for reducing risks of overeducation, horizontal and real mismatch than areas in Java (Jabodetabek, Gerbang Kertosusilo and Kedung Sepur). The presence of agglomeration economies correlates with a significant reduction in the job-education mismatch, with varying effects depending on the area..

Increasing demands for agricultural lands and built-up areas, driven by rapid population growth in developing countries including Indonesia, exacerbates the strain on tropical forests. Therefore, crucial to regular maintenance of forest monitoring is necessary to support sustainable forest management and minimize deforestation. In addition, driving factors of deforestation need to be comprehended and serve as considerations in the development of policies and decision-making. The main objective was to provide an in-depth understanding of the phenomenon of deforestation and its underlying variables in tropical regions, with a case study of Katingan Regency, Indonesia. Machine learning for remote sensing data analysis was integrated to investigate multi-temporal land cover in scouting deforestation and its driving factors. We found that the performance of random forests (RF) in all experimental settings was generally superior to support vector machines (SVM), achieving the best overall accuracy of 0.95. Land cover change analysis in the Katingan Regency (covering 2.04 M ha) suggested total deforestation during 2004−2022 of approximately 247.108 ha, an average of almost 14 thousand ha per year. Logistic regression showed that selected predictors significantly influenced the occurrence of deforestation. Non-forest areas devised a greater likelihood of deforestation than designated forest areas. Protected areas acted as an agent to minimize and impede regional deforestation. Meanwhile the probability of deforestation was greater on the outside of forest concession areas. We conclude that efforts to prevent deforestation need to be elevated, particularly in open-access production forests, characterized by high accessibility. In addition, the protection of the remaining forests, especially in non-forest designated areas, needs to be accommodated in regional spatial planning policies.

First phase of the grain for green (GFG) policy, one of the China’s forest policies, was implemented in the late 1990s and ended in 2012. The first phase of the GFG policy was successful from a macro perspective, although there were some failures. Based on these outcomes, the second phase of the GFG policy was implemented from 2014 to 2020. This study used panel data to develop an empirical land use model and conduct a comparative static analysis focusing on the GFG policy. Results of the static analysis confirmed factors that affect GFG for the years 2002–2018. In addition, differences in the explanatory variables between the first (2002–2012) and second periods (2014–2018) were determined. Furthermore, differences in GFG subsidies between the northern and southern provinces in the first phase were analyzed for their effects on a reforestation area. The main results revealed that the amount of investment in GFG and rural livelihood security had a positive effect on the expansion of the area of GFG. In addition, the amount of investment in GFG was more effective during the second period than the first period.

India is a developing market economy that comprised over 18% of the global population in 2020 and showed a 1.29% share of world GDP (Gross Domestic Product) in 1990. In addition, 3.20% of global energy consumption belonged to India in 1990. By 2020, India’s share of the world GDP was 3.08%, increasing its GDP by almost 3 times. However, energy usage increased by less than 2 times with a share of 6.25% in the world’s total energy consumption. Therefore, India managed to decrease its energy intensity per capita level by 64.35% in 2020 compared to 1990 by using less energy even with an increased income. In this context, this study investigated the question of how the Indian economy reduced its energy intensity for the period between 1990 and 2020. The impacts of GDP per capita, economic integration, foreign direct investments (FDI) and industrialization on energy intensity were analyzed using annual data from 1990 to 2020. First, the standard Augmented Dickey–Fuller Test (ADF) and Fourier ADF test methods were used to determine stationarity of the series. Then Fourier Autoregressive Distributed Lag (ADL) and Fourier Engle–Granger tests, recently introduced in the literature, were used to examine the cointegration relationships because all of the series were stable after subtracting the first differences. The results indicated a cointegration link between the variables. According to the empirical evidence obtained from FMOLS/CCR (DOLS) analysis, an increase of 1% in economic growth and foreign direct investment over the long run led to a decrease in energy intensity of approximately 1.08%/1.12% (1.14%) and 0.01%/0.001% (0.05%), respectively. Additionally, the results from FMOLS/CCR (DOLS) analysis indicated that a 1% rise in industrialization and trade openness in the long term resulted in an increase in energy intensity of approximately 0.25%/0.13% (0.39%) and 0.15%/0.18% (0.21%), respectively. Finally, fully modified ordinary least squares (FMOLS), Charnes, Cooper and Rhodes Model (CCR), and Stock-Watson Dynamic Ordinary Least Squares (DOLS) estimators were used for short and long-term coefficient estimations. Therefore, we conclude based on these findings that economic growth and foreign capital decrease energy intensity over the long term, while industrialization and economic integration increase energy intensity.